Bitcoin Price Forecasting: A Comparative Study of Machine Learning, Statistical and Deep Learning Models

Neelam Urooj; Laiba Asif; Zohra  Jabin

Authors

Neelam Urooj Institute of Business Management and Administrative Sciences (IBM & AS), The Islamia University of Bahawalpur, Bahawalpur, Pakistan
Laiba Asif School of Systems and Technology, University of the Management and Technology, Lahore, Pakistan
Zohra Jabin Institute of Business Management and Administrative Sciences (IBM & AS), The Islamia University of Bahawalpur, Bahawalpur, Pakistan

Keywords:

Bitcoin, Forecasting, Statistical Analysis, Machine Learning, Deep Learning

Abstract

Introduction/Importance of Study:

Cryptocurrency price prediction is crucial for investors and researchers, given the market's nonlinear nature and the potential for significant financial implications.

Novelty:

This study offers a novel approach to cryptocurrency price prediction, leveraging a range of machine learning and deep learning models to address the challenges of predicting Bitcoin's exchange rate.

Materials & Methods:

The study employs various machine learning and deep learning models, including Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), along with traditional models like Linear Regressor, Random Regressor, ExtraTreesClassifier, XGBoost Regressor, ARIMA, Prophet, and CNN.

Results & Discussion:

The ExtraTreesClassifier model emerged as the top performer, achieving a Test MAPE of 0.0689. This model outperformed deep learning models like RNNs, indicating its effectiveness in cryptocurrency price prediction.

Conclusion:

The findings suggest that the proposed models, particularly the ExtraTreesClassifier, can provide valuable insights for investors and traders in the cryptocurrency market.

References

S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System”, Accessed: Apr. 05, 2024. [Online]. Available: www.bitcoin.org

D. G. Baur and T. Dimpfl, “The volatility of Bitcoin and its role as a medium of exchange and a store of value,” Empir. Econ., vol. 61, no. 5, pp. 2663–2683, Nov. 2021, doi: 10.1007/S00181-020-01990-5.

“Bitcoin Laundering: An Analysis of Illicit Flows into Digital Currency Services”, Accessed: Apr. 05, 2024. [Online]. Available: https://www.mail-archive.com/cryptography@

M. Xu, X. Chen, and G. Kou, “A systematic review of blockchain,” Financ. Innov., vol. 5, no. 1, Dec. 2019, doi: 10.1186/S40854-019-0147-Z.

J. Y. Huang and J. H. Liu, “Using social media mining technology to improve stock price forecast accuracy,” J. Forecast., vol. 39, no. 1, pp. 104–116, Jan. 2020, doi: 10.1002/FOR.2616.

S. McNally, J. Roche, and S. Caton, “Predicting the Price of Bitcoin Using Machine Learning,” Proc. - 26th Euromicro Int. Conf. Parallel, Distrib. Network-Based Process. PDP 2018, pp. 339–343, Jun. 2018, doi: 10.1109/PDP2018.2018.00060.

Y. Chen, X. Xie, T. Zhang, J. Bai, and M. Hou, “A deep residual compensation extreme learning machine and applications,” J. Forecast., vol. 39, no. 6, pp. 986–999, Sep. 2020, doi: 10.1002/FOR.2663.

A. Aggarwal, I. Gupta, N. Garg, and A. Goel, “Deep Learning Approach to Determine the Impact of Socio Economic Factors on Bitcoin Price Prediction,” 2019 12th Int. Conf. Contemp. Comput. IC3 2019, Aug. 2019, doi: 10.1109/IC3.2019.8844928.

A. García-Medina and T. L. D. Huynh, “What drives bitcoin? An approach from continuous local transfer entropy and deep learning classification models,” Entropy, vol. 23, no. 12, p. 1582, Dec. 2021, doi: 10.3390/E23121582/S1.

E. Akyildirim, A. Goncu, and A. Sensoy, “Prediction of cryptocurrency returns using machine learning,” Ann. Oper. Res., vol. 297, no. 1–2, pp. 3–36, Feb. 2021, doi: 10.1007/S10479-020-03575-Y/TABLES/18.

“Bitcoin Historical Dataset.” Accessed: Apr. 05, 2024. [Online]. Available: https://www.kaggle.com/datasets/prasoonkottarathil/btcinusd

“(PDF) Forecasting accuracy of machine learning and linear regression: evidence from the secondary CAT bond market.” Accessed: Apr. 05, 2024. [Online]. Available: https://www.researchgate.net/publication/369010278_Forecasting_accuracy_of_machine_learning_and_linear_regression_evidence_from_the_secondary_CAT_bond_market

Shalabh, “A revisit to efficient forecasting in linear regression models,” J. Multivar. Anal., vol. 114, no. 1, pp. 161–170, Feb. 2013, doi: 10.1016/J.JMVA.2012.07.017.

Z. El Mrabet, N. Sugunaraj, P. Ranganathan, and S. Abhyankar, “Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems,” Sensors 2022, Vol. 22, Page 458, vol. 22, no. 2, p. 458, Jan. 2022, doi: 10.3390/S22020458.

T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., vol. 13-17-August-2016, pp. 785–794, Mar. 2016, doi: 10.1145/2939672.2939785.

M. Kumar and M. Thenmozhi, “Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models,” Int. J. Banking, Account. Financ., vol. 5, no. 3, pp. 284–308, 2014, doi: 10.1504/IJBAAF.2014.064307.

D. Chandola, A. Mehta, S. Singh, V. A. Tikkiwal, and H. Agrawal, “Forecasting Directional Movement of Stock Prices using Deep Learning,” Ann. Data Sci., vol. 10, no. 5, pp. 1361–1378, Oct. 2023, doi: 10.1007/S40745-022-00432-6/TABLES/3.

K. A. Al-Utaibi, S. Siddiq, and S. M. Sait, “Stock Price Forecasting with LSTM: A Brief Analysis of Mathematics Behind LSTM,” https://doi.org/10.1142/S1793048023410047, pp. 1–14, Aug. 2023, doi: 10.1142/S1793048023410047.

J. Hu, Q. Chang, and S. Yan, “A GRU-based hybrid global stock price index forecasting model with group decision-making,” Int. J. Comput. Sci. Eng., vol. 26, no. 1, pp. 12–19, 2023, doi: 10.1504/IJCSE.2023.129153.

J. Khan, E. Lee, and K. Kim, “A higher prediction accuracy–based alpha–beta filter algorithm using the feedforward artificial neural network,” CAAI Trans. Intell. Technol., vol. 8, no. 4, pp. 1124–1139, Dec. 2023, doi: 10.1049/CIT2.12148.

“3.3. Metrics and scoring: quantifying the quality of predictions — scikit-learn 1.4.1 documentation.” Accessed: Apr. 05, 2024. [Online]. Available: https://scikit-learn.org/stable/modules/model_evaluation.html

“Home - PyCaret.” Accessed: Apr. 05, 2024. [Online]. Available: https://pycaret.org/

M. A. Arshed, W. Qureshi, M. Rumaan, M. T. Ubaid, A. Qudoos, and M. U. G. Khan, “Comparison of Machine Learning Classifiers for Breast Cancer Diagnosis,” 4th Int. Conf. Innov. Comput. ICIC 2021, 2021, doi: 10.1109/ICIC53490.2021.9692926.

H. Younis, M. Asad Arshed, F. ul Hassan, M. Khurshid, H. Ghassan, and M. Haseeb-, “Tomato Disease Classification using Fine-Tuned Convolutional Neural Network,” Int. J. Innov. Sci. Technol., vol. 4, no. 1, pp. 123–134, Feb. 2022, doi: 10.33411/IJIST/2022040109.

A. Shahzad, M. A. Arshed, F. Liaquat, M. Tanveer, M. Hussain, and R. Alamdar, “Pneumonia Classification from Chest X-ray Images Using Pre-Trained Network Architectures,” VAWKUM Trans. Comput. Sci., vol. 10, no. 2, pp. 34–44, Dec. 2022, doi: 10.21015/VTCS.V10I2.1271.

M. Mubeen, M. A. Arshed, and H. A. Rehman, “DeepFireNet - A Light-Weight Neural Network for Fire-Smoke Detection,” Commun. Comput. Inf. Sci., vol. 1616 CCIS, pp. 171–181, 2022, doi: 10.1007/978-3-031-10525-8_14/COVER.

M. A. Arshed, S. Mumtaz, M. Hussain, R. Alamdar, M. T. Hassan, and M. Tanveer, “DeepFinancial Model for Exchange Rate Impacts Prediction of Political and Financial Statements,” 3rd IEEE Int. Conf. Artif. Intell. ICAI 2023, pp. 13–19, 2023, doi: 10.1109/ICAI58407.2023.10136658.

M. T. M. A. Arshed, A. Shahzad, K. Arshad, D. Karim, S. Mumtaz, “Multiclass Brain Tumor Classification from MRI Images using Pre-Trained CNN Model,” VFAST trans. softw. eng., vol. 10, no. 4, pp. 22–28, 2022.

M. A. Arshed and F. Riaz, “Machine Learning for High Risk Cardiovascular Patient Identification 1,” J. Distrib. Comput. Syst., vol. 4, no. 2, pp. 34–39, 2021, Accessed: Dec. 26, 2023. [Online]. Available: https://ijdcs.ir/wp-content/uploads/2022/08/IJDCS-8-6.pdf

H. M. H. J. Aslam, M. A. Arshed, S. Iqbal, “Deep Learning Based Multi-Class Eye Disease Classification: Enhancing Vision Health Diagnosis,” Tech. J., vol. 29, no. 1, pp. 7–12.

M. Hussain, A. Shahzad, F. Liaquat, M. A. Arshed, S. Mansoor, and Z. Akram, “Performance Analysis of Machine Learning Algorithms for Early Prognosis of Cardiac Vascular Disease,” Tech. J., vol. 28, no. 02, pp. 31–41, Jun. 2023, Accessed: Dec. 26, 2023. [Online]. Available: https://tj.uettaxila.edu.pk/index.php/technical-journal/article/view/1778

M. A. Arshed et al., “Machine Learning with Data Balancing Technique for IoT Attack and Anomalies Detection,” Int. J. Innov. Sci. Technol., vol. 4, no. 2, pp. 490–498, 2022, doi: 10.33411/ijist/2022040218.